
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE :		List[Any]       			=							logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE :		List[Any]       			=							'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase=8  ):
				A_													=	height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				A_													=	width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor






class        __lowercase		(       _A							):


					def __init__(						self							,							a__							,							a__							,							a__							,							)		->				Optional[Any]:





									'''simple docstring'''
									super().__init__()

									self.register_modules(
									    unet=UpperCamelCase__							,							scheduler=UpperCamelCase__							,							movq=UpperCamelCase__							,							)
									A_													=	2 ** (len(self.movq.config.block_out_channels    ) - 1)


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__							,							a__							,							a__							,							a__    )		->				Any:





									'''simple docstring'''






									if latents is None:
													A_													=	randn_tensor(UpperCamelCase__							,							generator=UpperCamelCase__							,							device=UpperCamelCase__							,							dtype=UpperCamelCase__    )
									else:
													if latents.shape != shape:
																	raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
													A_													=	latents.to(UpperCamelCase__    )

									A_													=	latents * scheduler.init_noise_sigma
									return latents


					def 							lowerCAmelCase_     (						self							,							a__=0    )		->				Tuple:





									'''simple docstring'''






									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

									A_													=	torch.device(F"cuda:{gpu_id}"    )

									A_													=	[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																	cpu_offload(UpperCamelCase__							,							UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self							,							a__=0    )		->				Tuple:





									'''simple docstring'''






									if is_accelerate_available() and is_accelerate_version('''>='''							,							'''0.17.0.dev0'''    ):
													from accelerate import cpu_offload_with_hook
									else:
													raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''    )

									A_													=	torch.device(F"cuda:{gpu_id}"    )

									if self.device.type != "cpu":
													self.to('''cpu'''							,							silence_dtype_warnings=UpperCamelCase__    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									A_													=	None
									for cpu_offloaded_model in [self.unet, self.movq]:
													A_						,       A_													=	cpu_offload_with_hook(UpperCamelCase__							,							UpperCamelCase__							,							prev_module_hook=UpperCamelCase__    )

									# We'll offload the last model manually.
									A_													=	hook


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''






									if not hasattr(self.unet							,							'''_hf_hook'''    ):
													return self.device
									for module in self.unet.modules():
													if (
													    hasattr(UpperCamelCase__							,							'''_hf_hook'''    )
													    and hasattr(module._hf_hook							,							'''execution_device'''    )
													    and module._hf_hook.execution_device is not None
													):
																	return torch.device(module._hf_hook.execution_device    )
									return self.device


					@torch.no_grad()
					@replace_example_docstring(UpperCamelCase__    )
					def __call__(						self							,							a__							,							a__							,							a__ = 5_1_2							,							a__ = 5_1_2							,							a__ = 1_0_0							,							a__ = 4.0							,							a__ = 1							,							a__ = None							,							a__ = None							,							a__ = "pil"							,							a__ = True							,							)		->				Optional[Any]:





									'''simple docstring'''
									A_													=	self._execution_device

									A_													=	guidance_scale > 1.0

									if isinstance(UpperCamelCase__							,							UpperCamelCase__    ):
													A_													=	torch.cat(UpperCamelCase__							,							dim=0    )
									A_													=	image_embeds.shape[0] * num_images_per_prompt
									if isinstance(UpperCamelCase__							,							UpperCamelCase__    ):
													A_													=	torch.cat(UpperCamelCase__							,							dim=0    )

									if do_classifier_free_guidance:
													A_													=	image_embeds.repeat_interleave(UpperCamelCase__							,							dim=0    )
													A_													=	negative_image_embeds.repeat_interleave(UpperCamelCase__							,							dim=0    )

									A_													=	torch.cat([negative_image_embeds, image_embeds]							,							dim=0    ).to(dtype=self.unet.dtype							,							device=UpperCamelCase__    )

									self.scheduler.set_timesteps(UpperCamelCase__							,							device=UpperCamelCase__    )
									A_													=	self.scheduler.timesteps

									A_													=	self.unet.config.in_channels

									A_						,       A_													=	downscale_height_and_width(UpperCamelCase__							,							UpperCamelCase__							,							self.movq_scale_factor    )

									# create initial latent
									A_													=	self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)							,							image_embeds.dtype							,							UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__							,							self.scheduler							,							)

									for i, t in enumerate(self.progress_bar(UpperCamelCase__    )    ):
													# expand the latents if we are doing classifier free guidance
													A_													=	torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

													A_													=	{'''image_embeds''': image_embeds}
													A_													=	self.unet(
													    sample=UpperCamelCase__							,							timestep=UpperCamelCase__							,							encoder_hidden_states=UpperCamelCase__							,							added_cond_kwargs=UpperCamelCase__							,							return_dict=UpperCamelCase__							,							)[0]

													if do_classifier_free_guidance:
																	A_						,       A_													=	noise_pred.split(latents.shape[1]							,							dim=1    )
																	A_						,       A_													=	noise_pred.chunk(2    )
																	A_						,       A_													=	variance_pred.chunk(2    )
																	A_													=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	A_													=	torch.cat([noise_pred, variance_pred_text]							,							dim=1    )

													if not (
													    hasattr(self.scheduler.config							,							'''variance_type'''    )
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																	A_						,       A_													=	noise_pred.split(latents.shape[1]							,							dim=1    )

													# compute the previous noisy sample x_t -> x_t-1
													A_													=	self.scheduler.step(
													    UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__							,							generator=UpperCamelCase__							,							)[0]
									# post-processing
									A_													=	self.movq.decode(UpperCamelCase__							,							force_not_quantize=UpperCamelCase__    )['''sample''']

									if output_type not in ["pt", "np", "pil"]:
													raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

									if output_type in ["np", "pil"]:
													A_													=	image * 0.5 + 0.5
													A_													=	image.clamp(0							,							1    )
													A_													=	image.cpu().permute(0							,							2							,							3							,							1    ).float().numpy()

									if output_type == "pil":
													A_													=	self.numpy_to_pil(UpperCamelCase__    )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=UpperCamelCase__    )

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

